This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.

Detailed Data Description

This data set is generated from brightness temperature data derived from the following sensors: the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR), the Defense Meteorological Satellite Program (DMSP) -F8, -F11 and -F13 Special Sensor Microwave/Imagers (SSM/Is), and the DMSP-F17 Special Sensor Microwave Imager/Sounder (SSMIS). The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.

This product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors. The data are generated using the NASA Team algorithm developed by the Oceans and Ice Branch, Laboratory for Hydrospheric Processes at NASA Goddard Space Flight Center (GSFC).

These data include gridded daily (every other day for SMMR data) and monthly averaged sea ice concentrations for both the north and south polar regions. The data are produced at GSFC about once per year, with roughly a one-year latency, and include data since 26 October 1978. Data are produced from SMMR brightness temperature data processed at NASA GSFC and from SSM/I and SSMIS brightness temperature data processed at the National Snow and Ice Data Center (NSIDC).

Data are scaled and stored as one-byte integers in flat binary arrays. For each data file, a corresponding PNG browse image file is provided.

Accounting for Sensor Differences

The goal of this data set is to provide a long term, consistent sea ice concentration product in which sea ice extent and area differences between the sensors are reduced and could serve as a baseline for future measurements. To achieve this, it is necessary to address differences between the SMMR and the DMSP-F8, -F11, and -F13 SSM/I sensors, as well as the DMSP-F17 SSMIS sensor. This document describes the basic characteristics of the SMMR, SSM/I, and SSMIS platforms and summarizes the problems encountered when deriving sea ice concentrations from brightness temperatures measured by sensors with different frequencies, different footprint sizes, different visit times, and different calibrations. A major obstacle to resolving these differences is the lack of sufficient overlapping data from sequential sensors. The techniques employed to solve these problems, or at least reduce their impacts, include:

Basic limitations also arise from the sensor resolution, temporal coverage, and algorithm assumptions and characteristics. The NASA Team algorithm is not designed to provide ice concentration for fresh-water ice (for example, lake and river ice). The filtering used to remove land-to-ocean spillover may affect the area of some open water features within the ice pack near coasts (coastal polynyas).

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Using These Data

Potential applications for these sea ice concentration data include:

Monitoring the distribution, extent, and area of the Arctic and Antarctic sea ice cover

Identifying and monitoring large, persistent open water areas surrounded by sea ice (polynyas)

Analyses of regional and global trends in sea ice cover

Validation of sea ice models and climate models

Analysis of sea ice/ocean and sea ice/atmosphere interactions

Users should be aware that the ice concentration maps were derived from algorithms that were "tuned" to minimize the differences in ice extent and ice covered area during the overlap periods when transitioning from one instrument to the next (overlap from SMMR to DMSP-F8 SSM/I, from DMSP-F8 to -F11 SSM/I, from DMSP-F11 to -F13 SSM/I, and from DMSP-F13 SSM/I to DMSP-F17 SSMIS). This does not mean that the ice concentrations themselves are well matched. See the Data Verification by Data Center section of this document for a summary of ice extent and ice covered area differences during the overlap periods.

It is also important to know that SMMR and SSM/I-SSMIS have different data gaps at the North Pole due to orbital differences. Therefore, any time series of parameters, such as ice extent and ice covered area, need to take these differences into account. A pole mask is provided for this purpose. See the Masks and Overlays section.

Particular care is needed to interpret the sea ice concentrations during summer when melt is present, and in regions where new sea ice makes up a substantial part of the sea ice cover. Some residual errors remain due to weather effects and mixing of ocean and land area within the sensor field of view, or FOV, and due to sensor differences.

It is recommended that sea ice extent and area be computed from daily maps of ice concentrations that are then used to compute monthly averages of those parameters. Computations of sea ice extents and sea ice areas should not be made from the monthly-averaged ice concentration maps because that may result in a biased time series.

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Format

Data are scaled, unsigned flat binary with one byte per pixel, and therefore have no byte order, or endianness. Data are stored as one-byte integers representing scaled sea ice concentration values. Range section for more information. For each data file, a corresponding browse image file in PNG format is also provided.

The file format consists of a 300-byte descriptive header followed by a two-dimensional array of one-byte values containing the data. The file header is composed of:

a 21-element array of 6-byte character strings that contain information such as polar stereographic grid characteristics

a 24-byte character string containing the file name

a 80-character string containing an optional image title

a 70-byte character string containing ancillary information such as data origin, data set creation date, etc.

For compatibility with ANSI C, IDL, and other languages, character strings are terminated with a NULL byte.

The file header can be accessed in a variety of ways. For example, it can be treated as a simple sequence of bytes containing ASCII character strings or as a complex data structure of arrays. Table 1 describes the file header.

Table 1. File Header Description

Bytes

Description

1-6

Missing data integer value

7-12

Number of columns in polar stereographic grid

13-18

Number of rows in polar stereographic grid

19-24

Unused/internal

25-30

Latitude enclosed by polar stereographic grid

31-36

Greenwich orientation of polar stereographic grid

37-42

Unused/internal

43-48

J-coordinate of the grid intersection at the pole

49-54

I-coordinate of the grid intersection at the pole

55-60

Five-character instrument descriptor (SMMR, SSM/I, SSMIS)

61-66

Two descriptors of two characters each that describe the data;
(for example, 07 cn = Nimbus-7 ice concentration)

67-72

Starting Julian day of grid data

73-78

Starting hour of grid data (if available)

79-84

Starting minute of grid data (if available)

85-90

Ending Julian day of grid data

91-96

Ending hour of grid data (if available)

97-102

Ending minute of grid data (if available)

103-108

Year of grid data

109-114

Julian day of grid data

115-120

Three-digit channel descriptor (000 for ice concentrations)

121-126

Integer scaling factor

127-150

24-character file name (without file-name extension)

151-230

80-character image title

231-300

70-character data information (creation date, data source, etc.)

The data can be read with image processing software by specifying a 300-byte header, with an image size of 304 columns x 448 rows for Arctic data and 316 columns x 332 rows for Antarctic data. For example, in a high-level programming language or image processing software, declare a 300-byte array for the header and a 304 x 448 Arctic image array. Read the 300-byte header array first, then read the image array.

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File and Directory Structure

Data are on the HTTPS site in the https://daacdata.apps.nsidc.org/pub/DATASETS/nsidc0051_gsfc_nasateam_seaice/ directory. Refer to Figure 1. Within this directory there is a final-gsfc directory that contains a north and south folder. The north and south folders are further separated into daily and monthly folders. The daily folder is also separated by year. For example, /nsidc0051_gsfc_nasateam_seaice/final-gsfc/north/daily/1990/. The final-gsfc directory also contains a browse folder. The browse folder contains browse PNG image files and follows the same file structure as the north and south folders.

Figure 1. Directory Structure

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File Naming Convention

This section explains the file naming convention used for this product with an example.

Each of the three instruments provide global coverage except for a circular sector centered over the North Pole. These sectors are never measured due to orbit inclination of the satellite. Table 4 shows the sizes and latitudes of each of the pole holes.

The SSMIS pole hole was implemented in March 2015 and applied to all data from January 2008 to present. Even though SSMIS data begin in January 2007, this product does not start using the SSMIS pole hole mask until January 2008 to allow for comparison analysis with SSM/I during the transition from SSM/I to SSMIS data in 2007.

Table 4. Pole Hole Sizes and Dates by Mask

Pole Hole Mask Name

Pole Hole Area
(million km2)

Pole Hole Radius
(km)

Latitude

Dates Used

SSMIS Pole Hole Mask

0.029

94

89.18° N

January 2008 to present

SSM/I Pole Hole Mask

0.31

311

87.2° N

July 1987 through December 2007

SMMR Pole Hole Mask

1.19

611

84.5° N

November 1978 through June 1987

Spatial Resolution

The spatial resolution for this data set is 25 km.

Projection and Grid Description

The sea ice concentration data are displayed in polar stereographic projection. For more information, see Polar Stereographic Projections and Grids. The grid size varies depending on the region, as shown in Table 5.

Table 5. Regional Grid Size

Region

Columns

Rows

North

304

448

South

316

332

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Temporal Coverage

Data are from 26 October 1978 through the most current processing. See the Data Acquisition Methods section for dates by instrument and platform.

Temporal Resolution

The SMMR instrument scanner operated only on alternate days, due to spacecraft power limitations. Therefore, SMMR data were only collected every other day. Typically, there are at least 14 days of coverage per month, although there are major data gaps in August of 1982 (04, 08, and 16 August 1982), and in August of 1984 (13 through 23 August 1984) for both polar regions.

SSM/I data were collected daily and SSMIS data continue to be collected daily. A major data gap in the SSM/I data exists from 03 December 1987 to 13 January 1988. For the latest details regarding data gaps, refer to the SSM/I-SSMIS Brightness Temperature Data Availability Web page.

Sea ice concentrations are provided for each day of data and also as monthly means. The monthly means are generated by averaging all the available daily files for each individual month, excluding pixels of missing data. Refer to the Monthly Data Generation section of this document for more information.

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Parameter or Variable

Parameter Description

Sea ice concentration represents an areal coverage of sea ice. For a given grid cell, the parameter provides an estimate of the fractional amount of sea ice covering that cell, with the remainder of the area consisting of open ocean. Land areas are coded with a land mask value.

Parameter Source

Parameter Range

Data are stored as one-byte integers representing sea ice concentration values. The sea ice concentration data are packed into byte format by multiplying the derived fractional sea ice concentration floating-point values (ranging from 0.0 to 1.0) by a scaling factor of 250. For example, a sea ice concentration value of 0.0 (0%) maps to a stored one-byte integer value of 0, and a sea ice concentration value of 1.0 (100%) maps to a stored one-byte integer value of 250. To convert to the fractional parameter range of 0.0 to 1.0, divide the scaled data in the file by 250. To convert to percentage values (0% to 100%), divide the scaled data in the file by 2.5.

Data files may contain integers from 0 to 255, as described in Table 6.

Table 6. Description of Data Values

Data Value

Description

0 - 250

Sea ice concentration (fractional coverage scaled by 250)

251

Circular mask used in the Arctic to cover the irregularly-shaped data gap around the pole (caused by the orbit inclination and instrument swath)

252

Unused

253

Coastlines

254

Superimposed land mask

255

Missing data

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Data Validation by Source

The performance of the NASA Team algorithm was assessed in numerous studies such as Cavalieri et al. 1992, and these results apply to this data set. However, improvements in this data set that differ from previous studies include the minimization of coastal and open-ocean influences that tend to yield inaccurate sea ice concentrations. Visual data checking was used to assess the performance of these modifications.

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Confidence Level/Accuracy Judgment

Estimates of the accuracy of the NASA Team algorithm vary depending on sea ice conditions, methods, and locations used in individual studies. Cavalieri et al. (1992) summarizes several of these studies. In general, accuracy of total sea ice concentration is within +/- 5 percent of the actual sea ice concentration in winter, and +/- 15 percent in the Arctic during summer when melt ponds are present on the sea ice. Accuracy tends to be best within the consolidated ice pack when the sea ice is relatively thick (greater than 20 cm) and ice concentration is high. Accuracy decreases as the proportion of thin ice increases. See Cavalieri et al. (1992),Steffen et al. (1992), and other listed references for an overview of the algorithm performance.

Some weather-related effects and land contamination are still present. The amount and spatial distribution of remaining weather effects vary with season. Also, occasional bad scan lines still appear in the data. Based on NSIDC analyses, some sensor-to-sensor differences are likely to remain in these data, particularly for marginal ice zones. See NSIDC Special Report 5: An Intercomparison of DMSP F11- and F13-derived Sea Ice Products for summaries of differences among the SSM/I sensors.

Residual weather effects and processing errors in May 1986 data result in large bands of very low ice concentrations over the open ocean in the Weddell, Bellingshausen, and Amundsen seas in the Southern Hemisphere. Although the magnitude of these false ice concentrations is less than one percent, users should be aware that such errors do occur in data for many days within that month.

Overlap periods exist when transitioning from one instrument to the next. These overlaps are from SMMR to DMSP-F8 SSM/I, from DMSP-F8 to -F11 SSM/I, from DMSP-F11 to -F13 SSM/I, and from DMSP-F13 SSM/I to DMSP-F17 SSMIS. During overlap periods, data were available from two instruments, although good data may not be available from both instruments during the entire operating overlap. Differences in ice covered area and ice extent during the overlap periods were minimized by tuning the sea ice algorithms. Wavelet analysis of the time series of ice extent and ice covered area show no significant offsets between the different satellites.

Software and Tools

Software and tools for reading and displaying the files are located in the tools directory on the FTP site (Fig. 2). Software includes IDL routines to ingest and read sea ice concentration data. Masks and overlays are also provided.

Table 9 lists the tools that can be used with this data set. For a comprehensive list of all polar stereographic tools and for more information, see the Polar Stereographic Data Tools Web page.

Data Acquisition and Processing

Theory of Measurements

The SMMR, SSM/I, and SSMIS instruments are microwave radiometers that sense emitted microwave radiation. This radiation is affected by surface and atmospheric conditions, and thus provides a range of geophysical information.

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Source/Platform

The Nimbus-7 and DMSP F-series spacecraft fly in near-polar sun-synchronous orbits; details their respective orbits are compared in Table 10.

Table 10. Comparison of Orbital Parameters

Parameter

Nimbus-7

DMSP-F8

DMSP-F11

DMSP-F13

DMSP-F17

Nominal Altitude1

955 km

860 km

830 km

850 km

850 km

Inclination Angle

99.1 degrees

98.8 degrees

98.8 degrees

98.8 degrees

98.8 degrees

Orbital Period

104 minutes

102 minutes

101 minutes

102 minutes

102 minutes

Ascending Node Equatorial Crossing
(Local Time)

Approx. 12:00 p.m.

Approx. 6:00 a.m.

Approx. 5:00 p.m.

Approx. 5:43 p.m.

Approx. 5:31 p.m.

Algorithm Frequencies1

18.0, 37.0 GHz

19.3, 37.0 GHz

19.3, 37.0 GHz

19.3, 37.0 GHz

19.3, 37.0 GHz

Earth Incidence Angle1

50.2

53.1

52.8

53.4

53.1

3 dB Beam Width (Degrees)1

1.6, 0.8

1.9, 1.1

1.9, 1.1

1.9, 1.1

1.9, 1.1

1 Indicates sensor and spacecraft orbital characteristics of the sensors used in generating the sea ice concentrations.

The SSMIS sensor is a conically-scanning passive microwave radiometer that harnesses the imaging and sounding capabilities of three previous DMSP microwave sensors, including the SSMI, the SSM/T-1 temperature sounder, and the SSMI/T-2 moisture sounder. The SSMIS sensor measures microwave energy at 24 frequencies from 19 to 183 GHz with a swath width of 1700 km. Please refer to the SSMIS Instrument Description Web page for more details. Tables 11 and 12 give the FOV of each instrument.

Table 11. SSM/I and SSMIS FOV

Frequency

Footprint Size

19.3 GHz

70x45 km

22.2 GHz

60x40 km

37.0 GHz

38x30 km

Table 12. SMMR FOV

Frequency

Footprint Size

6.6 GHz

148x95 km

10.7 GHz

91x59 km

18.0 GHz

55x41 km

21.0 GHz

46x30 km

37.0 GHz

27x18 km

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Data Acquisition Methods

The combined SMMR, SSM/I, and SSMIS sea ice concentration time series is produced from brightness temperatures obtained from GSFC and NSIDC. The four sets of satellite data currently used to create this data stream and the time periods for which the data are available are shown in Table 13.

Sea ice concentrations for this data set were produced using a revised NASA Team algorithm that uses a different set of tie points and weather filters than the original NASA Team algorithm. See the NASA Team Sea Ice Algorithm for a description of the original algorithm. The NASA Technical Memorandum 104647 includes information about differences, such as tie points, between the original algorithm and the revised NASA Team algorithm. In addition, the NASA Team algorithm uses different channels of the SMMR and the SSM/I-SSMIS brightness temperature data:

Table 14. SMMR and SSM/I-SSMIS Brightness Temperature Channels

Instrument

Channels

SMMR

Vertically and horizontally polarized (v-pol and h-pol) 18.0 GHz

V-pol 37.0 GHz

SSM/I

V-pol and h-pol 19.3 GHz

V-pol 37.0 GHz

SSMIS

V-pol and h-pol 19.3 GHz

V-pol 37.0 GHz

The weather filter used for the SMMR (Gloersen and Cavalieri 1986) was found to be inadequate for the SSM/I due to the SSM/I's use of the 19.3 GHz channel (which is further up on the shoulder of the water vapor line at 22.2 GHz) rather than the 18.0 GHz channel. A different weather filter is used to reduce spurious sea ice concentrations from SSM/I that result from the presence of atmospheric water vapor, non-precipitating cloud liquid water, rain, and sea surface roughening by surface winds. This filter is a combination of the SSM/I 37.0 and 19.3 GHz channels, which effectively eliminates most of the spurious sea ice concentration measurements resulting from wind-roughening of the ocean surface, cloud liquid water, and rainfall. Another filter that is based on the 19.3 and 22.2 GHz channels is also used. The rationale behind combining the 19.3 and 22.2 GHz channels is based on the sensitivity of the 22.2 GHz to water vapor and on the need to minimize the effect of ice temperature variations at the ice edge.

Processing Steps

Platform and Sensor Differences

Comparisons of sea ice concentrations calculated for each sensor during overlap periods using published algorithm tie points reveal significant differences. These may result from differences in sensor and orbital characteristics, differences in observation times (and therefore tidal effects), and/or differences in algorithm coefficients. Sensor and orbital characteristic differences for the Nimbus-7 SMMR and DMSP-F8 SSM/I include antenna beam width, channel frequency, spacecraft altitude, ascending node time, and angle of incidence. In addition, the sea ice algorithm tie points are significantly different. The DMSP sensors also differ in ascending node time, altitude, and angle of incidence. Because the visit times of the DMSP satellites occur during different phases of the diurnal cycle, tidal effects may result in differences in the sea ice distribution. GSFC presumes that any such effects are mitigated by the correction scheme described below. The Comparison of Orbital Parameters table in the Source/Platform sectionsummarizes sensor and orbital characteristic differences. The GSFC processing attempts to accommodate for these differences in each pair of sensors by employing a set of algorithm tie points determined through linear relationships between the observed brightness temperatures during the overlap periods.

Nimbus-7 SMMR to DMSP-F8 SSM/I Transition

Daily brightness temperature maps from the Nimbus-7 SMMR and from the DMSP-F8 SSM/I during their period of overlap, 09 July to 20 August 1987, were compared for both the Arctic and Antarctic. Unfortunately, there were only 22 days of common coverage. A linear, least-squares best-fit of the cumulative data was obtained for each of the corresponding channels. For the purpose of eliminating spurious brightness temperatures resulting from residual land spillover effects, an Arctic land mask that expanded three to four pixels out from the original land mask was used in the determination of the best fit between the two data streams.

The eliminated pixels represent only a very small fraction of the total number of sea ice concentration pixels, but eliminating them helps considerably in reducing the outliers on the scatter plots. These linear relations were used to generate a set of SSM/I tie points that are consistent with the original SMMR sea ice algorithm tie points (Gloersen et al. 1992). The published DMSP-F8 SSM/I tie points (Cavalieri et al. 1992) were not used. In addition to using these transformations, the DMSP-F8 SSM/I open water tie points were subjectively tuned to help minimize the differences between the SMMR and DMSP-F8 SSM/I sea ice extent and area during the overlap period. In all cases, except for the Antarctic DMSP-F8 SSM/I values, the tuned amount is within one standard error of estimate. GSFC suspects the reason for the larger tuned values results from greater weather effects during the overlap period.

DMSP-F8 to DMSP-F11 SSM/I Transition

The transition period from DMSP-F8 to -F11 includes only 16 days of good data overlap, from 03 to 18 December 1991. The DMSP-F11 SSM/I open water tie points were also tuned to help reduce differences in sea ice extent and area as was done with the DMSP-F8 SSM/I values. A further adjustment to the Antarctic 37V sea ice type-B F11 tie point was also made to reduce the sea ice area difference. In this case, the amount of tuning needed to reduce the sea ice extent and area differences between the DMSP-F8 and -F11 values is well within one standard error of estimate.

DMSP-F11 to DMSP-F13 SSM/I Transition

The effects of changing from the DMSP-F11 to the -F13 satellite were examined for a 5-month overlap period, from 5 May 1995 through 30 September 1995. Generally, in terms of hemispheric averages of mean ice concentration, the biases introduced by the transition are slight and not statistically significant; however, in some regions relatively large and significant differences are seen. In addition, differences in sea ice extent and total ice covered area between the two platforms were found to be statistically significant. For more information, please see NSIDC Special Report 5: An Intercomparison of DMSP F11- and F13-derived Sea Ice Products.

DMSP-F13 SSM/I to DMSP-F17 SSMIS Transition

The effects of changing from the DMSP-F13 SSM/I to the -F17 SSMIS were examined for a 12-month overlap period, from 01 January 2007 to 31 December 2007. Differences in sea ice extent and total ice covered area between the two platforms and instruments were found to be statistically significant, though fairly similar when compared with previous intersensor calibrations conducted for this time series (Cavalieri et al. 1999). Earlier intersensor calibrations, however, were limited by relatively short periods of sensor overlap (such as sixteen days) and could thus account for less agreement with this transition (Cavalieri et al. 2012). In addition, earlier agreement may be due to the subjective tuning of some tie-points that was required in past intercalibrations (Cavalieri et al. 1999).

Land-to-Ocean Spillover and Residual Weather-Related Effects

The next step in preparing the data is the correction for land-to-ocean spillover, often referred to as "land contamination," and residual weather-related effects. While these steps eliminate much of the land-to-ocean spillover and weather effects over open ocean, these problems are not entirely removed. See the section Data Verification by Data Center for additional comments.

Land-to-Ocean Spillover

Land-to-ocean spillover refers to the issue of the blurring of sharp contrasts in brightness temperature, such as those that exist between land and ocean, due to the relatively coarse width of the sensor antenna pattern. This problem is of concern because it results in false sea ice signals along coastlines because both land and sea ice have much higher brightness temperatures than ocean. The method used to reduce the spillover is an extension of the method employed for the single-channel Nimbus-5 Electrically Scanning Microwave Radiometer (ESMR) data in Parkinson et al. (1987). Figure 2a illustrates the effect of the coarse resolution of the microwave antenna on a coastline resulting in false sea ice signals in the vicinity of the coast, and Figure 2b shows the seven-by-seven array used in the procedure to reduce the land-to-ocean spillover effect. The rationale behind the approach is that a minimum observed sea ice concentration in the vicinity of coastlines where no sea ice remains offshore, which is generally seen in late summer, is probably the result of land spillover; so it is subtracted from the image. To reduce the error of subtracting sea ice in areas of actual sea ice cover, the technique searches for and requires the presence of open water in the vicinity of the image pixel to be corrected.

Land-to-ocean spillover was reduced by the following three-step procedure:

A matrix M was created covering the entire grid and identifying each pixel as land, shore, near-shore, offshore, or non-coastal ocean. The identification of land pixels was straightforward; they were obtained from the land/sea mask. The identification of shore, near-shore, and offshore pixels was based on the scheme shown in Figure 2b, where the pixel to be identified is labeled I,J. This pixel is considered a shore pixel if any pixel adjacent to it is land, a near-shore pixel if none of the A pixels are land but at least one of the B pixels is land, and an offshore pixel if none of the A or B pixels are land but at least one of the C pixels is land. All other ocean pixels are considered non-coastal ocean. This matrix M is created once and used throughout the data set.

A matrix CMIN, to represent minimum sea ice concentrations on a pixel-by-pixel basis throughout the entire grid, was created for each instrument type. CMIN was created by first constructing a matrix P containing the minimum monthly average sea ice concentrations throughout a given year, then adjusting that matrix at offshore, near-shore, and shore pixels. In the case of SMMR, 1984 monthly data were used, whereas in the case of SSM/I, 1992 monthly data were used. In both cases, the adjustments were as follows: (a) at offshore pixels, any P values exceeding 20 percent were reduced to 20 percent; (b) at near-shore pixels, any P values exceeding 40 percent were reduced to 40 percent; and (c) at shore pixels, any P values exceeding 60% were reduced to 60 percent. The CMIN matrix was created once for SMMR and once for SSM/I, then used throughout the data set.

The daily sea ice concentration matrices were adjusted at any offshore, near-shore, and shore pixels in the vicinity of open water. Specifically, the neighborhoodof an offshore pixel was defined as containing the 8 other pixels in the 3 x 3 box centered on the offshore pixel; the neighborhood of a near-shore pixel was defined as containing the 24 other pixels in the 5 x 5 box centered on the near-shore pixel; and the neighborhood of a shore pixel was defined as containing the 48 other pixels in the 7 x 7 box centered on the shore pixel. At any time when the neighborhood of an offshore, near-shore, or shore pixel contains three or more open-water pixels (sea ice concentration less than 15 percent), then the calculated sea ice concentration at the offshore, near-shore, or shore pixel is reduced by the value for that pixel in the matrix CMIN. Wherever the subtraction leads to negative sea ice concentrations, the concentrations are set to 0 percent. This land-spillover correction algorithm is clearly a rough approximation, as the contaminated amount does not stay constant over time; but the scheme has been found to reduce substantially the spurious sea ice concentrations on the grids.

Figure 2a. Effect of the coarse resolution of the microwave antenna on a coastline resulting in false sea ice signals in the vicinity of the coast.Figure 2b. Seven-by-seven array used in the procedure to reduce the land-to-ocean spillover effect.Click image for larger version.

Residual Weather-Related Effects

Weather effects can cause the passive microwave signature of seawater to appear like that of ice (Cavalieri 1995). A correction is made for removing spurious ice resulting from residual weather effects that were missed by the automatic weather filters. These valid ice masks are based on monthly climatological Sea Surface Temperatures (SSTs) from the NOAA Ocean Atlas (Levitus and Boyer 1994). These data, originally on a two-degree by two-degree grid, were remapped onto the SSM/I grid. Because the SST data did not extend to the SSM/I coastline, the data were extrapolated to the coastline once they were mapped onto the SSM/I grid. The SST maps are used as follows:

In the Northern Hemisphere, in any pixel where the monthly SST is greater than 278 K, the sea ice concentration is set to zero throughout the month.

In the Southern Hemisphere, in any pixel where the monthly SST is greater than 275 K, the sea ice concentration is set to zero throughout the month.

The higher threshold SST value was needed in the Northern Hemisphere because the 275 K isotherm used in the south was too close to the sea ice edge in the north. In a few instances, corrections to the regridded SST data were needed, because otherwise actual sea ice was being lost.

Figure 3a shows a sample sea ice concentration field without the land-spillover and residual weather correction, and Figure 3b shows that same sample after the correction is applied.

Manual Quality Control

The automated residual weather removal procedures unfortunately do not remove all spurious ice. In the older data in particular, clearly erroneous sea ice concentration values occurred on occasion due to bad input brightness temperatures, sometimes even with entire scans or swaths being dominated by bad data. Consequently, a manual quality control procedure was used to remove these erroneous data. This is done by visual inspection of the ice concentration images. In some instances, the erroneous nature of the data is quite clear, while in other instances it is less definitive. In the latter cases, a decision to retain or remove the suspect data is made based on consistency or lack of consistency with the data for the preceding and succeeding days. For data deemed to be spurious in the open ocean region, the ice concentrations are zeroed out. Data deemed to be erroneous within the ice cover are flagged as missing data and are filled like other data gaps, as described below.

Filling Data Gaps

Figure 3a. Sea Ice Concentration Map of the Arctic for 01 August 1983 before the Application of the Land-spillover and Residual Weather CorrectionsFigure 3b.After Corrections

There are instances of missing data. In some cases whole days (or weeks or months) are missing. In other cases, large swaths or wedges of missing data exist within an image, along with scattered pixels of missing data throughout the grid. The scattered pixels of missing data, resulting generally from mapping the orbital data to the SSM/I grid, were filled by applying a spatial linear interpolation scheme on the brightness temperature maps. The larger areas of missing data, resulting from gaps between orbital swaths (generally at low latitudes on daily maps) or from partial coverage or missing days, were filled by temporal interpolation on the sea ice concentration maps. No data at all were available for the period from 02 December 1987 through 12 January 1988. This gap was not filled by temporal linear interpolation; instead it was left as missing data.

Monthly Data Generation

Once daily data have been processed as previously described, monthly data are generated. Monthly averaged sea ice concentration grids are produced from an average of the daily sea ice concentration grids available for each month. Monthly files for both hemispheres are provided for every month beginning October 1978. However, for October 1978, December 1987 and January 1988, the time series was incomplete: only three days of data were available during October 1978 to generate the monthly mean, only two days were available for December 1987, and only 19 days were available for January 1988. Therefore, the monthly means for these months do not represent the "true" monthly means.

In most cases, GSFC used all daily data to compute monthly averaged sea ice concentrations from a particular instrument until the data were no longer available. For example, SMMR data were used to compute monthly sea ice concentrations until the instrument stopped collecting data on 20 August 1987. Beginning 21 August 1987, SSM/I data were used. In 1991, DMSP-F8 SSM/I data were used through December 18; beginning December 19, DMSP-F11 SSM/I data were used.

Note: It is recommended that sea ice extent and area be computed from daily maps of ice concentrations that are then used to compute monthly averages of those parameters. Computations of sea ice extents and sea ice areas should not be made from the monthly-averaged ice concentration maps because that may result in a biased time series.

Error Sources

File Errors

Table 12 describes files that have been found to contain errors and that have been corrected during the life of this data set along with the types of errors that were corrected. NSIDC recommends data users download the corrected files for these dates listed in Table 15. In addition, more detail on the updates can be found in Table 16.

Table 15. Description of Corrected Files

File Date

File Name

Type of Correction

Date Correction was Made

1984-09

nt_198409_n07_v01_n.bin

Geolocation error

July 2014

1984-14-09

nt_19840914_n07_v01_n.bin

Geolocation error

July 2014

1983-07-30

nt_19830730_n07_v01_n.bin

Weather correction

January 2013

1984-07-26

nt_19840726_n07_v01_n.bin

Weather correction

January 2013

1984-07-28

nt_19840728_n07_v01_n.bin

Weather correction

January 2013

1984-07-30

nt_19840730_n07_v01_n.bin

Weather correction

January 2013

1985-07-01

nt_19850701_n07_v01_n.bin

Coastal/weather correction

January 2013

1985-07

nt_198507_n07_v01_n.bin

Coastal/weather correction

January 2013

1987-07-21

nt_19870721_f08_v01_n.bin

Weather correction

January 2013

1987-12-01

nt_19871201_f08_v01_n.bin

Ambiguous; not a clear source of error

January 2013

1987-12-01

nt_19871201_f08_v01_s.bin

Ambiguous; not a clear source of error

January 2013

1995-11-02

nt_19951102_f13_v01_n.bin

Ambiguous; not a clear source of error

January 2013

1995-11-14

nt_19951114_f13_v01_n.bin

Ambiguous; not a clear source of error

January 2013

1995-11

nt_199511_f13_v01_n.bin

Ambiguous; not a clear source of error

January 2013

1995-12-07

nt_19951207_f13_v01_n.bin

Land/coastal correction

January 2013

1996-04-10

nt_19960410_f13_v01_n.bin

Land/coastal correction

January 2013

1996-04-23

nt_19960423_f13_v01_n.bin

Land/coastal correction

January 2013

1996-05-09

nt_19960509_f13_v01_s.bin

Land/coastal correction

January 2013

1996-05

nt_199605_f13_v01_s.bin

Land/coastal correction

January 2013

1996-06-12

nt_19960612_f13_v01_n.bin

Land/coastal correction

January 2013

1996-06-18

nt_19960618_f13_v01_n.bin

Land/coastal correction; same pixels as 1996-06-12

January 2013

1996-06-19

nt_19960619_f13_v01_n.bin

Ambiguous; not a clear source of error

January 2013

1996-06-20

nt_19960620_f13_v01_n.bin

Land/coastal correction

January 2013

1996-06

nt_199606_f13_v01_n.bin

Land/coastal correction

January 2013

1996-10-06

nt_19961006_f13_v01_s.bin

Land/coastal correction

January 2013

1996-10

nt_199610_f13_v01_s.bin

Land/coastal correction

January 2013

1996-11-01

nt_19961101_f13_v01_n.bin

Land/coastal correction

January 2013

1996-11-06

nt_19961106_f13_v01_n.bin

Land/coastal correction

January 2013

1996-11-14

nt_19961114_f13_v01_n.bin

Ambiguous; not a clear source of error

January 2013

1996-12-05

nt_19961205_f13_v01_n.bin

Land/coastal correction; same pixels as 1996-06-12

January 2013

1996-12-23

nt_19961223_f13_v01_n.bin

Land/coastal correction; same pixels as 1996-06-12

January 2013

Sea Ice Pixel Correction

Additional Quality Control (QC) of the data set was done from 1978 to 2014 to remove additional spurious ice. Figures 4 and 5 show the number of pixels that are different across the time series due to the additional QC.

Figure 4. Number of Pixels that are Different Across the Time Series for the Northern HemisphereClick image for larger version.Figure 5. Number of Pixels that are Different Across the Time Series for the Southern HemisphereClick image for larger version.

Background color on

Processing History

Table 16 summarizes the Version history for this product.

Table 16. Processing History

Version

Date

Description

V1.1

December 2015

Data were reprocessed from 1978 to 2014, and additional QC of the data set was done to remove additional spurious ice. Refer to Figure 4and Figure 5, which show the number of pixels that are different across the time series due to the additional QC. In addition, the data set no longer includes overlap dates during satellite transition periods. Refer to Table 13 for the time periods of each satellite used in this data set. This version also extends the temporal coverage to 31 May 2015.

An error was found in the sea ice concentration field for 14 September 1984. Due to a geolocation error in the source data, several hundred thousand square kilometers of erroneous ice occurred in that data. The original file has been removed and replaced with an average of the two files nearest in time (September 12 and 16). The monthly September 1984 average concentration field was reprocessed using the replaced September 14 data. See Table 12 for the file names and the correction made.

V1

June 2014

The browse images for the entire record have been reprocessed to include a title and simplified color bar; the data were not affected.

V1

January 2013

NSIDC applied corrections to 29 files that showed errors in a previous release of these data. The errors occurred in files from both SMMR (1983 – 1985) and SSM/I (1995 – 1996). See Table 12 for a list of these files.

Cavalieri, D. J., and C. L. Parkinson. 1987. On the Relationship Between Atmospheric Circulation and the Fluctuations in the Sea Ice Extents of the Bering and Okhotsk Seas. Journal of Geophysical Research 92: 7141-7162.

Cavalieri, D. J. and S. Martin. 1994. The Contribution of Alaskan, Siberian, and Canadian Coastal Polynyas to the Cold Halocline Layer of the Arctic Ocean. Journal of Geophysical Research 99:18,343-18,362.

Gloersen, P., and W. J. Campbell. 1988a. Variations in the Arctic, Antarctic, and Global Sea Ice Covers During 1978-1987 as Observed with the Nimbus-7 Scanning Multichannel Microwave Radiometer. Journal of Geophysical Research 93:10,666-10,674.

Gloersen, P., and W. J. Campbell. 1991a. Variations of Extent, Area, and Open Water of the Polar Sea Ice Covers: 1978-1987, Proc. of the Int. Conf. on the Role of the Polar Regions in Global Change, G. Weller, C. L. Wilson, and B. A. B. Severin, eds.,Geophysical Institute, University of Fairbanks, Alaska. 778 pages.

Zwally, H. J., T. T. Wilheit, P. Gloersen, and J. l. Mueller. 1976. Characteristics of Antarctic Sea Ice as Determined by Satellite-borne Microwave Imagers, in Proceedings of the Symposium on Meteorological Observations from Space: Their Contribution to the First GARP Global Experiment, Committee on Space Research of the International Council of Scientific Unions, Philadelphia, 94-97.

Document Information

DOCUMENT CREATION DATE

DOCUMENT REVISION DATE

December 2015
June 2014
October 2011
March 2011
April 2008
October 2006

No technical references available for this data set.

How To

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